Enterprises, researchers, and various other entities increasingly deal with the challenge of organizing large sets of data in meaningful ways. Clustering is one technique that is frequently used to organize data by finding intrinsic grouping among data samples. Creating clusters typically involves evaluating a set of data samples and grouping samples that are related to each other.
Clusters may be formed using a variety of algorithms and techniques, such as the furthest-point-first algorithm, which is a fast variant of the k-means algorithm. Many traditional clustering solutions may use the furthest-point-first algorithm as a stepping stone to other types of more effective clustering algorithms (e.g., hierarchical clustering algorithms). While these traditional approaches may eventually produce useful clusters, they may be time and resource intensive. Furthermore, traditional clustering approaches mail fail to yield optimized cluster groupings and/or sizes. Accordingly, the instant disclosure identifies and addresses a need for additional and improved systems and methods for clustering data.